sgsR - Structurally Guided SamplingTristan Goodbody, Nicholas Coops, Martin Queinnec, Joanne White, Piotr Tompalski, Andrew Hudak, David Auty, Ruben Valbuena, Antoine LeBoeuf, Ian Sinclair, Grant McCartney, Jean-Francois Prieur, Murray Woods
University of British Columbia
September 2nd, 2022 @ Berlin
sgsR is an R-package developed to implement structurally guided sampling approaches for enhanced forest inventories.sgsR stands for structurally guided sampling implemented in R
Stratification and sampling functions to guide primarily model-based sampling approaches
Focus on management-level inventories
Funded by the Canadian Wood Fibre Centre, Canadian Forest Service
Brief inventory and sampling overview
Discuss using auxiliary variables within sampling frameworks
Structurally guided sampling using Airborne Laser Scanning
sgsR overview
Programmatic examples of the package
Purpose: Obtain knowledge about the population (forest area) under investigation and provide estimates of specific target variables
Needed information: Defined by the scope & scale of the inventory. Answered by questions like:
Who/what is the information for? (e.g. Reporting obligations, timber production)
How big of an area are we inventorying? (e.g. National level, operational level)
Mensuration is a cornerstone of forest management
Sampling can be:
Labour intensive
Logistically challenging
Expensive
Randomized
Sampling unit probabilities is equal, known, or can be known
Different methods exist (e.g. simple random, systematic)
Time-tested
Simple
Efficient
Broadly used
Imagery (satellite, airborne, drone)
Feature-based inventories (species, management type)
ALS metrics (height, cover, variability)
✔️ Understand inventory attributes of interest
✔️ Associate auxiliary data correlated to those attributes
✔️ Sample across the full range of attribute variability
“Our results highlight that LiDAR data integrated with field data sampling designs can provide broad-scale assessments of vegetation structure and biomass, i.e., information crucial for carbon and biodiversity science.” (Hawbaker et al. 2009)
“The ALS data also provides an excellent source of prior information that may be used in the design phase of the field survey to reduce the size of the field data set.”(Gobakken, Korhonen, and Næsset 2013)
#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mr$zq90, # p90
nStrata = 5) # 5 strata in p90#--- perform dual metric stratification ---#
sraster <- strat_quantiles(mraster = mr$zq90, # p90
mraster2 = mr$zsd, # standard deviation of height
nStrata = 10, # 10 strata in p90
nStrata2 = 3) # 3 strata in zsd#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mr$zq90, # p90
mraster2 = mr$zsd, # standard deviation of height
nStrata = 10, # 10 strata in p90
nStrata2 = 3) # 3 strata in zsd
#--- structurally guided stratified sampling ---#
sample_strat(sraster = sraster, nSamp = 100, plot = TRUE)sgsRsgsR purpose sgsR is a toolbox to provide primarily model-based sampling approaches for management-level forest inventories that are:
Transparent
Repeatable
Tuneable
Spatially-explicit
sgsR was built using the terra, sf, & tidyverse packages
There are 4 primary function verbs that sgsR uses:
strat_* - apply stratification to metrics raster (mraster) and output a stratified raster (sraster)
sample_* - allocate samples using srasters produced from strat_* functions
calculate_*- calculate sample information or create useful intermediary sampling products
extract_* - extract pixels values from rasters to samples
sgsR overview
1️⃣ Read in some ALS metrics
1️⃣ Read in some ALS metrics
#--- Stratification ---#
#--- Load ALS metrics from sgsR internal data ---#
r <- system.file("extdata", "mraster.tif", package = "sgsR")
#--- Read ALS metrics using the terra package ---#
mraster <- terra::rast(r)2️⃣ Read in a linear road access network
#--- Load access network from sgsR internal data ---#
a <- system.file("extdata", "access.shp", package = "sgsR")
#--- load the access vector using the sf package ---#
access <- sf::st_read(a)3️⃣ Stratify p90 in to 4 strata based on quantiles
#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mraster$zq90, # input ALS metric - p90
nStrata = 4) # desired number of strata (4)4️⃣ Now lets use the sraster output
#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mraster$zq90, # input ALS metric - p90
nStrata = 4) # desired number of strata (4)
#--- perform sampling ---#
samples <- sample_strat(sraster = sraster,
nSamp = 100,
allocation = "proportional", # equal, manual, optimal
access = access,
buff_inner = 50,
buff_outer = 400)5️⃣ Request 100 samples
#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mraster$zq90, # input ALS metric - p90
nStrata = 4) # desired number of strata (4)
#--- perform sampling ---#
samples <- sample_strat(sraster = sraster,
nSamp = 100,
allocation = "prop", # equal, manual, optimal
access = access,
buff_inner = 50,
buff_outer = 400)6️⃣ Sample proportional to stratum size
#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mraster$zq90, # input ALS metric - p90
nStrata = 4) # desired number of strata (4)
#--- perform sampling ---#
samples <- sample_strat(sraster = sraster,
nSamp = 100,
allocation = "prop", # equal, manual, optimal
access = access,
buff_inner = 50,
buff_outer = 400)7️⃣ Bring in the access road
#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mraster$zq90, # input ALS metric - p90
nStrata = 4) # desired number of strata (4)
#--- perform sampling ---#
samples <- sample_strat(sraster = sraster,
nSamp = 100,
allocation = "prop", # equal, manual, optimal
access = access,
buff_inner = 50,
buff_outer = 400)8️⃣ Specify we don’t want samples within 50 m of access
#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mraster$zq90, # input ALS metric - p90
nStrata = 4) # desired number of strata (4)
#--- perform sampling ---#
samples <- sample_strat(sraster = sraster,
nSamp = 100,
allocation = "prop", # equal, manual, optimal
access = access,
buff_inner = 50,
buff_outer = 400)9️⃣ Or further than 400 m from access
#--- perform stratification ---#
sraster <- strat_quantiles(mraster = mraster$zq90, # input ALS metric - p90
nStrata = 4) # desired number of strata (4)
#--- perform sampling ---#
samples <- sample_strat(sraster = sraster,
nSamp = 100,
allocation = "prop", # equal, manual, optimal
access = access,
buff_inner = 50,
buff_outer = 400)Mapped result (A) and plotted result (B)
Note buffered access in A. Points are samples in both A & B
Cumulative frequency distributions
access constrained vs full extent for p90 (A) and zsd (B)
existing sampleexisting sample“I have an existing sample network, can I use those same sample locations?”
“If I go and visit those same sample units, where should I locate new samples for structural representation?”
existing sampleLets create an existing sample of 50 plots using simple random sampling (sample_srs)
We are assuming these have been measured or used previously and can be revisited
existing sampleAdapted Hypercube Evaluation of a Legacy Sample (AHELS) (Malone, Minansy, and Brungard 2019)
sample_ahels() works by:
Determining representation of existing sample
Generate quantile and covariance matrix of ALS metrics
Determining number of additional samples that can / need to be added
Identify where new samples are needed to balance quantile density and sampling density
Iteratively locate samples
existing sample1️⃣ We have our existing sample
existing sample2️⃣ Now we can use the sample_ahels() algorithm with our ALS metrics
existing sample3️⃣ Specify our existing sample
existing sample4️⃣ And specify we want 50 new sample units (nSamp)
existing sampleMapped result (A) and plotted result (B)
Note ratios (black/red) and additional added samples (e.g. n = 2)
sample_ahels() resultexisting only (A) and addition of new samples (B)
We see that metric and sample density become quite even - structurally representative
sgsR package provides many methods to implement SGS approachessgsR functionalitySpecial thanks to the Canadian Wood Fibre Centre for funding this research!
My Twitter: @GoodbodyT
IRSS Twitter: @IRSS_UBC
Special thanks to my collaborators
This presentation was made with Quarto and will be made available on Github following the presentation at ForestSAT